Strategic Customer Recommendations in Online Service Platform
45 Pages Posted: 26 May 2020
Date Written: April 18, 2020
In online service platforms, economic inefficiency arises when customers are not fully aware of their preferences - customers may choose an unsuitable service among horizontally differentiated ones. With its expertise or data dominance, a platform can be more informed about customers’ hidden preferences and in turn, provide service consultations to customers. From a customer-centric perspective, we focus on the effects of service consultations on customers’ queue-joining behaviors and social welfare.
We propose a queueing game model wherein customers make Bayesian belief updates based on a platform’s recommendations, to decide between joining two horizontally differentiated queues. When the customers self-select their favorite service, their queue-joining behaviors impose negative externalities through congestion, which poses a welfare gap towards "the first best". Our results indicate that service consultations navigate the customers towards the more appropriate service, thus improving matching efficiency, reducing congestion cost and enhancing the total customer welfare. We further study how the platform should strategically release (partial) information by making personalized service recommendations to the customers. Surprisingly, we identify the "value of ignorance" when a customer-centric platform maximizes the aggregate customer welfare by strategically withholding service consultation results from a subset of the customers. These customers turn out to be the most flexible ones, who can correct the over-crowding queue-joining behaviors when set uninformed.
Keywords: service consultations, Hotelling model, Bayesian learning, game with incomplete information, targeted information disclosure
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